Smoothness Priors and the Distributed Lag Estimator.

Abstract

Shiller's distributed lag estimator based on a smoothness prior demonstrates the potential of the Bayesian approach to statistical model building. Nevertheless, when the number of significant lag coefficients is small the assumption of smoothness of the pattern of the lag coefficients may not be appropriate. In this paper, to cover such a situation, the smoothness is assumed for the behavior of the coefficients viewed in the frequency domain. This definition leads to a smoothness prior with a particularly simple form. Numerical result shows that the estimator based on this smoothness prior produces good estimates of the lag coefficients where Shiller's prior produces highly biased estimates. It is also observed that the new estimator produces reasonable results even when the Shiller's prior is more appropriate. The danger of introducing a bias by assuming a Bayesian model is stressed in the discussion. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1979
Accession Number
ADA075574

Entities

People

  • Hirotugu Akaike

Organizations

  • Stanford University

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Networks
  • Coefficients
  • Computations
  • Contracts
  • Distribution Functions
  • Estimators
  • Frequency
  • Frequency Domain
  • Mathematics
  • Military Research
  • Models
  • Probabilistic Models
  • Probability
  • Statistical Analysis
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Seismology

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms